{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 82332 entries, 0 to 82331\n",
      "Data columns (total 6 columns):\n",
      "sttl                82332 non-null int64\n",
      "ct_dst_sport_ltm    82332 non-null int64\n",
      "ct_src_dport_ltm    82332 non-null int64\n",
      "swin                82332 non-null int64\n",
      "dwin                82332 non-null int64\n",
      "label               82332 non-null int64\n",
      "dtypes: int64(6)\n",
      "memory usage: 3.8 MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from __future__ import print_function\n",
    "\n",
    "df = pd.read_csv('miniLab.csv') # read in the csv file\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShuffleSplit(82332, n_iter=3, test_size=0.2, random_state=None)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import ShuffleSplit\n",
    "\n",
    "# we want to predict the X and y data as follows:\n",
    "if 'label' in df:\n",
    "    y = df['label'].values # get the labels we want\n",
    "    del df['label'] # get rid of the class label\n",
    "    X = df.values # use everything else to predict!\n",
    "\n",
    "    ## X and y are now numpy matrices, by calling 'values' on the pandas data frames we\n",
    "    #    have converted them into simple matrices to use with scikit learn\n",
    "    \n",
    "    \n",
    "# to use the cross validation object in scikit learn, we need to grab an instance\n",
    "#    of the object and set it up. This object will be able to split our data into \n",
    "#    training and testing splits\n",
    "num_cv_iterations = 3\n",
    "num_instances = len(y)\n",
    "cv_object = ShuffleSplit(n=num_instances,\n",
    "                         n_iter=num_cv_iterations,\n",
    "                         test_size  = 0.2)\n",
    "                         \n",
    "print(cv_object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====Iteration 0  ====\n",
      "accuracy 0.763041233983\n",
      "confusion matrix\n",
      " [[4928 2476]\n",
      " [1426 7637]]\n",
      "====Iteration 1  ====\n",
      "accuracy 0.766745612437\n",
      "confusion matrix\n",
      " [[4936 2471]\n",
      " [1370 7690]]\n",
      "====Iteration 2  ====\n",
      "accuracy 0.771178721079\n",
      "confusion matrix\n",
      " [[4968 2428]\n",
      " [1340 7731]]\n"
     ]
    }
   ],
   "source": [
    "# run logistic regression and vary some parameters\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics as mt\n",
    "\n",
    "# first we create a reusable logisitic regression object\n",
    "#   here we can setup the object with different learning parameters and constants\n",
    "lr_clf = LogisticRegression(penalty='l2', C=1.0, class_weight=None) # get object\n",
    "\n",
    "# now we can use the cv_object that we setup before to iterate through the \n",
    "#    different training and testing sets. Each time we will reuse the logisitic regression \n",
    "#    object, but it gets trained on different data each time we use it.\n",
    "\n",
    "iter_num=0\n",
    "# the indices are the rows used for training and testing in each iteration\n",
    "for train_indices, test_indices in cv_object: \n",
    "    # I will create new variables here so that it is more obvious what \n",
    "    # the code is doing (you can compact this syntax and avoid duplicating memory,\n",
    "    # but it makes this code less readable)\n",
    "    X_train = X[train_indices]\n",
    "    y_train = y[train_indices]\n",
    "    \n",
    "    X_test = X[test_indices]\n",
    "    y_test = y[test_indices]\n",
    "    \n",
    "    # train the reusable logisitc regression model on the training data\n",
    "    lr_clf.fit(X_train,y_train)  # train object\n",
    "    y_hat = lr_clf.predict(X_test) # get test set precitions\n",
    "\n",
    "    # now let's get the accuracy and confusion matrix for this iterations of training/testing\n",
    "    acc = mt.accuracy_score(y_test,y_hat)\n",
    "    conf = mt.confusion_matrix(y_test,y_hat)\n",
    "    print(\"====Iteration\",iter_num,\" ====\")\n",
    "    print(\"accuracy\", acc )\n",
    "    print(\"confusion matrix\\n\",conf)\n",
    "    iter_num+=1\n",
    "    \n",
    "# Also note that every time you run the above code\n",
    "#   it randomly creates a new training and testing set, \n",
    "#   so accuracy will be different each time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====Iteration 0  ====\n",
      "accuracy 0.764437966843\n",
      "confusion matrix\n",
      " [[4907 2516]\n",
      " [1363 7681]]\n",
      "====Iteration 1  ====\n",
      "accuracy 0.764134329265\n",
      "confusion matrix\n",
      " [[4862 2565]\n",
      " [1319 7721]]\n",
      "====Iteration 2  ====\n",
      "accuracy 0.7669885225\n",
      "confusion matrix\n",
      " [[4837 2508]\n",
      " [1329 7793]]\n"
     ]
    }
   ],
   "source": [
    "# this does the exact same thing as the above block of code, but with shorter syntax\n",
    "\n",
    "for iter_num, (train_indices, test_indices) in enumerate(cv_object):\n",
    "    lr_clf.fit(X[train_indices],y[train_indices])  # train object\n",
    "    y_hat = lr_clf.predict(X[test_indices]) # get test set precitions\n",
    "\n",
    "    # print the accuracy and confusion matrix \n",
    "    print(\"====Iteration\",iter_num,\" ====\")\n",
    "    print(\"accuracy\", mt.accuracy_score(y[test_indices],y_hat)) \n",
    "    print(\"confusion matrix\\n\",mt.confusion_matrix(y[test_indices],y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.76243396  0.76966053  0.76298051]\n"
     ]
    }
   ],
   "source": [
    "# and here is an even shorter way of getting the accuracies for each training and test set\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "accuracies = cross_val_score(lr_clf, X, y=y, cv=cv_object) # this also can help with parallelism\n",
    "print(accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.76401287  0.76571324  0.77044999]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.lr_explor>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here we can change some of the parameters interactively\n",
    "from ipywidgets import widgets as wd\n",
    "\n",
    "def lr_explor(cost):\n",
    "    lr_clf = LogisticRegression(penalty='l2', C=cost, class_weight=None) # get object\n",
    "    accuracies = cross_val_score(lr_clf,X,y=y,cv=cv_object) # this also can help with parallelism\n",
    "    print(accuracies)\n",
    "\n",
    "wd.interact(lr_explor,cost=(0.001,5.0,0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sttl has weight of 0.00801078761549\n",
      "ct_dst_sport_ltm has weight of 1.33435470422\n",
      "ct_src_dport_ltm has weight of -0.217149025977\n",
      "swin has weight of -0.0260784864882\n",
      "dwin has weight of 0.0251439957709\n"
     ]
    }
   ],
   "source": [
    "# interpret the weights\n",
    "\n",
    "# iterate over the coefficients\n",
    "weights = lr_clf.coef_.T # take transpose to make a column vector\n",
    "variable_names = df.columns\n",
    "for coef, name in zip(weights,variable_names):\n",
    "    print(name, 'has weight of', coef[0])\n",
    "    \n",
    "# does this look correct? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dwaraka\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.770571445922\n",
      "[[4951 2445]\n",
      " [1333 7738]]\n",
      "sttl has weight of 0.818421598665\n",
      "ct_src_dport_ltm has weight of -1.37212805506\n",
      "dwin has weight of 1.99851873575\n",
      "swin has weight of -2.15539180316\n",
      "ct_dst_sport_ltm has weight of 5.90406986657\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# we want to normalize the features based upon the mean and standard deviation of each column. \n",
    "# However, we do not want to accidentally use the testing data to find out the mean and std (this would be snooping)\n",
    "# to Make things easier, let's start by just using whatever was last stored in the variables:\n",
    "##    X_train , y_train , X_test, y_test (they were set in a for loop above)\n",
    "\n",
    "# scale attributes by the training set\n",
    "scl_obj = StandardScaler()\n",
    "scl_obj.fit(X_train) # find scalings for each column that make this zero mean and unit std\n",
    "# the line of code above only looks at training data to get mean and std and we can use it \n",
    "# to transform new feature data\n",
    "\n",
    "X_train_scaled = scl_obj.transform(X_train) # apply to training\n",
    "X_test_scaled = scl_obj.transform(X_test) # apply those means and std to the test set (without snooping at the test set values)\n",
    "\n",
    "# train the model just as before\n",
    "lr_clf = LogisticRegression(penalty='l2', C=0.05) # get object, the 'C' value is less (can you guess why??)\n",
    "lr_clf.fit(X_train_scaled,y_train)  # train object\n",
    "\n",
    "y_hat = lr_clf.predict(X_test_scaled) # get test set precitions\n",
    "\n",
    "acc = mt.accuracy_score(y_test,y_hat)\n",
    "conf = mt.confusion_matrix(y_test,y_hat)\n",
    "print('accuracy:', acc )\n",
    "print(conf )\n",
    "\n",
    "# sort these attributes and spit them out\n",
    "zip_vars = zip(lr_clf.coef_.T,df.columns) # combine attributes\n",
    "zip_vars.sort(key = lambda t: np.abs(t[0])) # sort them by the magnitude of the weight\n",
    "for coef, name in zip_vars:\n",
    "    print(name, 'has weight of', coef[0]) # now print them out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Hjh1PudzSNEtubq4WLVqkhx9+uN5FDgCwj6W/wV577TVVVVVp2rRpko5/CPqzn/3MlmAA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\naaPMXWz27Nnyer3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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb14a5b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now let's make a pandas Series with the names and values, and plot them\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "\n",
    "weights = pd.Series(lr_clf.coef_[0],index=df.columns)\n",
    "weights.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dO55yuaVplry8PC1cuFAPP/xwvYscAGAfS3+Dvfbaa6qqqtK0adMkHf8Q9Gc/+5ktwQDY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb161850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# you can also apply the StandardScaler function insied of the validation loop \n",
    "#  but this requires the use of PipeLines in scikit. Here is an example, but we will go over more \n",
    "#  thorough examples later in class\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "std_scl = StandardScaler()\n",
    "lr_clf = LogisticRegression(penalty='l2', C=0.05) \n",
    "\n",
    "# create the pipline\n",
    "piped_object = Pipeline([('scale', std_scl), ('logit_model', lr_clf)])\n",
    "\n",
    "# run the pipline corssvalidated\n",
    "for iter_num, (train_indices, test_indices) in enumerate(cv_object):\n",
    "    piped_object.fit(X[train_indices],y[train_indices])  # train object\n",
    "    \n",
    "# it is a little odd getting trained objects from a  pipeline:\n",
    "trained_model_from_pipeline = piped_object.named_steps['logit_model']\n",
    "\n",
    "# now look at the weights\n",
    "weights = pd.Series(trained_model_from_pipeline.coef_[0],index=df.columns)\n",
    "weights.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# okay, so run through the cross validation loop and set the training and testing variable for one single iteration\n",
    "for train_indices, test_indices in cv_object: \n",
    "    # I will create new variables here so that it is more obvious what \n",
    "    # the code is doing (you can compact this syntax and avoid duplicating memory,\n",
    "    # but it makes this code less readable)\n",
    "    X_train = X[train_indices]\n",
    "    y_train = y[train_indices]\n",
    "    \n",
    "    X_test = X[test_indices]\n",
    "    y_test = y[test_indices]\n",
    "    \n",
    "X_train_scaled = scl_obj.transform(X_train) # apply to training\n",
    "X_test_scaled = scl_obj.transform(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.763162689014\n",
      "[[4753 2597]\n",
      " [1303 7814]]\n"
     ]
    }
   ],
   "source": [
    "# lets investigate SVMs on the data and play with the parameters and kernels\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# train the model just as before\n",
    "svm_clf = SVC(C=0.5, kernel='linear', degree=3, gamma='auto') # get object\n",
    "svm_clf.fit(X_train_scaled, y_train)  # train object\n",
    "\n",
    "y_hat = svm_clf.predict(X_test_scaled) # get test set precitions\n",
    "\n",
    "acc = mt.accuracy_score(y_test,y_hat)\n",
    "conf = mt.confusion_matrix(y_test,y_hat)\n",
    "print('accuracy:', acc )\n",
    "print(conf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30649, 5)\n",
      "(30649,)\n",
      "[15329 15320]\n"
     ]
    }
   ],
   "source": [
    "# look at the support vectors\n",
    "print(svm_clf.support_vectors_.shape)\n",
    "print(svm_clf.support_.shape)\n",
    "print(svm_clf.n_support_ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.91017426  2.73610011 -1.29204511 -1.30620767  1.30742507]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdc53e90>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yssVigdFo7PH7NQ0ZBv2qTYpq0ukkOBxOZe8xaDB8FfwcauBYtGr63yU4ykqUvYdOgrcK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\nEQmEoU9EJBCGPhGRQBj6REQC+T9y7wjVzqdldgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc53410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# if using linear kernel, these make sense to look at (not otherwise, why?)\n",
    "print(svm_clf.coef_)\n",
    "weights = pd.Series(svm_clf.coef_[0],index=df.columns)\n",
    "weights.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking that SVM's choosen we find some patterns and an outliear within the abbreviated five featrue dataset.  That outlier being 'ct_dst_sport_ltm' equaling 2.7361.  The data was not normalized and the value recieved is 1.4361 greater than 'dwin', which is the second highest value at 1.30.  Beyond 'dwin' coming in with the second highest value, its value was inversely equal to 'swin', which was -1.30.  Additionally, 'swin' and 'ct_src_dport_ltm' were almost equal with a differnce of .008.  No relationship can be established in the correlation of numbers, beyond the labeling devices used and the number patterns received post analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30649 entries, 75555 to 61446\n",
      "Data columns (total 6 columns):\n",
      "sttl                30649 non-null int64\n",
      "ct_dst_sport_ltm    30649 non-null int64\n",
      "ct_src_dport_ltm    30649 non-null int64\n",
      "swin                30649 non-null int64\n",
      "dwin                30649 non-null int64\n",
      "label               30649 non-null int64\n",
      "dtypes: int64(6)\n",
      "memory usage: 1.6 MB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dwaraka\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Now let's do some different analysis with the SVM and look at the instances that were chosen as support vectors\n",
    "\n",
    "# now lets look at the support for the vectors and see if we they are indicative of anything\n",
    "# grabe the rows that were selected as support vectors (these are usually instances that are hard to classify)\n",
    "\n",
    "# make a dataframe of the training data\n",
    "df_tested_on = df.iloc[train_indices] # saved from above, the indices chosen for training\n",
    "# now get the support vectors from the trained model\n",
    "df_support = df_tested_on.iloc[svm_clf.support_,:]\n",
    "\n",
    "df_support['label'] = y[svm_clf.support_] # add back in the 'Survived' Column to the pandas dataframe\n",
    "df['label'] = y # also add it back in for the original data\n",
    "df_support.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ePXswe/ZshIWF4bHHHkNxcTE6dOiAKVOm4PTp0+jSpQtefvllofypU6ewevVq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3148b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nVrdr1w6bN29Gv379sHnzZpsTY7tSDnP2e4LtO6/9mtx3Z7dfkfzFYrGYy5cv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\nX9Ja2/X7AhpHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x140a40b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zlSNGxlNVZRiTRzIlk5RB1UZRFNP1xiOY6gJEZGYsw8ypqsowNluTKVk8LXsk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\n28cHeQqTRyKqfr+ey0VYrkNLHiPUs0i/KMgr5OC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C5w7Toat06yNdeke3ftGtS3Tti/7W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\nBzsN/8IunOArAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x165c40b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Tpkxx2v7dd98hJSUFd911FwBg9OjRWLVqFbKzs3HPPffUO87u3btx6tQpjBo1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\nrZHAc/K4jgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bead70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NhsiYx5kthMnbzmDpvrOeDoVdh+sv9+OEjzEfNn36dKxcubLepRcyMzMxatQo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\nScAnhBBCCLHMScAnhBBCCLHM/T+VwDwLIJVU7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe4bd4b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now lets see the statistics of these attributes\n",
    "from pandas.tools.plotting import boxplot\n",
    "\n",
    "# group the original data and the support vectors\n",
    "df_grouped_support = df_support.groupby(['label'])\n",
    "df_grouped = df.groupby(['label'])\n",
    "\n",
    "# plot KDE of Different variables\n",
    "vars_to_plot = ['sttl','ct_dst_sport_ltm','ct_src_dport_ltm','swin','dwin']\n",
    "\n",
    "for v in vars_to_plot:\n",
    "    plt.figure(figsize=(10,4))\n",
    "    # plot support vector stats\n",
    "    plt.subplot(1,2,1)\n",
    "    ax = df_grouped_support[v].plot.kde() \n",
    "    plt.legend(['No Attack','Attack'])\n",
    "    plt.title(v+' (Instances chosen as Support Vectors)')\n",
    "    \n",
    "    # plot original distributions\n",
    "    plt.subplot(1,2,2)\n",
    "    ax = df_grouped[v].plot.kde() \n",
    "    plt.legend(['No Attack','Attack'])\n",
    "    plt.title(v+' (Original)')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Kernal=rbf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# okay, so run through the cross validation loop and set the training and testing variable for one single iteration\n",
    "for train_indices, test_indices in cv_object: \n",
    "    # I will create new variables here so that it is more obvious what \n",
    "    # the code is doing (you can compact this syntax and avoid duplicating memory,\n",
    "    # but it makes this code less readable)\n",
    "    X_train = X[train_indices]\n",
    "    y_train = y[train_indices]\n",
    "    \n",
    "    X_test = X[test_indices]\n",
    "    y_test = y[test_indices]\n",
    "    \n",
    "X_train_scaled = scl_obj.transform(X_train) # apply to training\n",
    "X_test_scaled = scl_obj.transform(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.819821461104\n",
      "[[6393  971]\n",
      " [1996 7107]]\n"
     ]
    }
   ],
   "source": [
    "# lets investigate SVMs on the data and play with the parameters and kernels\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# train the model just as before / Kernal=rbf\n",
    "svm_clf = SVC(kernel='rbf') # get object\n",
    "svm_clf.fit(X_train_scaled, y_train)  # train object\n",
    "\n",
    "y_hat = svm_clf.predict(X_test_scaled) # get test set precitions\n",
    "\n",
    "acc = mt.accuracy_score(y_test,y_hat)\n",
    "conf = mt.confusion_matrix(y_test,y_hat)\n",
    "print('accuracy:', acc )\n",
    "print(conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Kernal=polynomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# okay, so run through the cross validation loop and set the training and testing variable for one single iteration\n",
    "for train_indices, test_indices in cv_object: \n",
    "    # I will create new variables here so that it is more obvious what \n",
    "    # the code is doing (you can compact this syntax and avoid duplicating memory,\n",
    "    # but it makes this code less readable)\n",
    "    X_train = X[train_indices]\n",
    "    y_train = y[train_indices]\n",
    "    \n",
    "    X_test = X[test_indices]\n",
    "    y_test = y[test_indices]\n",
    "    \n",
    "X_train_scaled = scl_obj.transform(X_train) # apply to training\n",
    "X_test_scaled = scl_obj.transform(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.809558510961\n",
      "[[6194 1246]\n",
      " [1890 7137]]\n"
     ]
    }
   ],
   "source": [
    "# lets investigate SVMs on the data and play with the parameters and kernels\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# train the model just as before / Kernal=polynomial\n",
    "svm_clf = SVC(kernel='poly') # get object\n",
    "svm_clf.fit(X_train_scaled, y_train)  # train object\n",
    "\n",
    "y_hat = svm_clf.predict(X_test_scaled) # get test set precitions\n",
    "\n",
    "acc = mt.accuracy_score(y_test,y_hat)\n",
    "conf = mt.confusion_matrix(y_test,y_hat)\n",
    "print('accuracy:', acc )\n",
    "print(conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Kernal=sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# okay, so run through the cross validation loop and set the training and testing variable for one single iteration\n",
    "for train_indices, test_indices in cv_object: \n",
    "    # I will create new variables here so that it is more obvious what \n",
    "    # the code is doing (you can compact this syntax and avoid duplicating memory,\n",
    "    # but it makes this code less readable)\n",
    "    X_train = X[train_indices]\n",
    "    y_train = y[train_indices]\n",
    "    \n",
    "    X_test = X[test_indices]\n",
    "    y_test = y[test_indices]\n",
    "    \n",
    "X_train_scaled = scl_obj.transform(X_train) # apply to training\n",
    "X_test_scaled = scl_obj.transform(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.719985425396\n",
      "[[5716 1749]\n",
      " [2862 6140]]\n"
     ]
    }
   ],
   "source": [
    "# lets investigate SVMs on the data and play with the parameters and kernels\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# train the model just as before / Kernal=sigmoid\n",
    "svm_clf = SVC(kernel='sigmoid') # get object\n",
    "svm_clf.fit(X_train_scaled, y_train)  # train object\n",
    "\n",
    "y_hat = svm_clf.predict(X_test_scaled) # get test set precitions\n",
    "\n",
    "acc = mt.accuracy_score(y_test,y_hat)\n",
    "conf = mt.confusion_matrix(y_test,y_hat)\n",
    "print('accuracy:', acc )\n",
    "print(conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Reg\n",
    "#Class weight = balanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====Iteration 0  ====\n",
      "accuracy 0.773304184126\n",
      "confusion matrix\n",
      " [[5277 2135]\n",
      " [1598 7457]]\n",
      "====Iteration 1  ====\n",
      "accuracy 0.774458006923\n",
      "confusion matrix\n",
      " [[5306 2077]\n",
      " [1637 7447]]\n",
      "====Iteration 2  ====\n",
      "accuracy 0.772757636485\n",
      "confusion matrix\n",
      " [[5269 2145]\n",
      " [1597 7456]]\n"
     ]
    }
   ],
   "source": [
    "# run logistic regression and vary some parameters\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics as mt\n",
    "\n",
    "# first we create a reusable logisitic regression object\n",
    "#   here we can setup the object with different learning parameters and constants\n",
    "lr_clf = LogisticRegression(penalty='l2', C=1.0, class_weight='balanced') # get object\n",
    "\n",
    "# now we can use the cv_object that we setup before to iterate through the \n",
    "#    different training and testing sets. Each time we will reuse the logisitic regression \n",
    "#    object, but it gets trained on different data each time we use it.\n",
    "\n",
    "iter_num=0\n",
    "# the indices are the rows used for training and testing in each iteration\n",
    "for train_indices, test_indices in cv_object: \n",
    "    # I will create new variables here so that it is more obvious what \n",
    "    # the code is doing (you can compact this syntax and avoid duplicating memory,\n",
    "    # but it makes this code less readable)\n",
    "    X_train = X[train_indices]\n",
    "    y_train = y[train_indices]\n",
    "    \n",
    "    X_test = X[test_indices]\n",
    "    y_test = y[test_indices]\n",
    "    \n",
    "    # train the reusable logisitc regression model on the training data\n",
    "    lr_clf.fit(X_train,y_train)  # train object\n",
    "    y_hat = lr_clf.predict(X_test) # get test set precitions\n",
    "\n",
    "    # now let's get the accuracy and confusion matrix for this iterations of training/testing\n",
    "    acc = mt.accuracy_score(y_test,y_hat)\n",
    "    conf = mt.confusion_matrix(y_test,y_hat)\n",
    "    print(\"====Iteration\",iter_num,\" ====\")\n",
    "    print(\"accuracy\", acc )\n",
    "    print(\"confusion matrix\\n\",conf)\n",
    "    iter_num+=1\n",
    "    \n",
    "# Also note that every time you run the above code\n",
    "#   it randomly creates a new training and testing set, \n",
    "#   so accuracy will be different each time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Reg\n",
    "#Class weight = dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====Iteration 0  ====\n",
      "accuracy 0.774397279407\n",
      "confusion matrix\n",
      " [[5004 2364]\n",
      " [1351 7748]]\n",
      "====Iteration 1  ====\n",
      "accuracy 0.765409607093\n",
      "confusion matrix\n",
      " [[4870 2541]\n",
      " [1322 7734]]\n",
      "====Iteration 2  ====\n",
      "accuracy 0.765409607093\n",
      "confusion matrix\n",
      " [[4985 2519]\n",
      " [1344 7619]]\n"
     ]
    }
   ],
   "source": [
    "# run logistic regression and vary some parameters\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics as mt\n",
    "\n",
    "# first we create a reusable logisitic regression object\n",
    "#   here we can setup the object with different learning parameters and constants\n",
    "lr_clf = LogisticRegression(penalty='l2', C=1.0, class_weight=None) # get object\n",
    "\n",
    "# now we can use the cv_object that we setup before to iterate through the \n",
    "#    different training and testing sets. Each time we will reuse the logisitic regression \n",
    "#    object, but it gets trained on different data each time we use it.\n",
    "\n",
    "iter_num=0\n",
    "# the indices are the rows used for training and testing in each iteration\n",
    "for train_indices, test_indices in cv_object: \n",
    "    # I will create new variables here so that it is more obvious what \n",
    "    # the code is doing (you can compact this syntax and avoid duplicating memory,\n",
    "    # but it makes this code less readable)\n",
    "    X_train = X[train_indices]\n",
    "    y_train = y[train_indices]\n",
    "    \n",
    "    X_test = X[test_indices]\n",
    "    y_test = y[test_indices]\n",
    "    \n",
    "    # train the reusable logisitc regression model on the training data\n",
    "    lr_clf.fit(X_train,y_train)  # train object\n",
    "    y_hat = lr_clf.predict(X_test) # get test set precitions\n",
    "\n",
    "    # now let's get the accuracy and confusion matrix for this iterations of training/testing\n",
    "    acc = mt.accuracy_score(y_test,y_hat)\n",
    "    conf = mt.confusion_matrix(y_test,y_hat)\n",
    "    print(\"====Iteration\",iter_num,\" ====\")\n",
    "    print(\"accuracy\", acc )\n",
    "    print(\"confusion matrix\\n\",conf)\n",
    "    iter_num+=1\n",
    "    \n",
    "# Also note that every time you run the above code\n",
    "#   it randomly creates a new training and testing set, \n",
    "#   so accuracy will be different each time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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